roiextract.pipeline.CentroidAggregation#

class roiextract.pipeline.CentroidAggregation(surf='sphere')#

Centroid-based aggregation of reconstructed source time courses within the ROI. The time course of the source that is the closest to the center of mass of the ROI is selected as the representative time course of the ROI.

Parameters:

surfstr, default=”sphere”: The surface to use for computing the center of mass. The provided value is forwarded to mne.Label.center_of_mass() without modification.

Methods

`fit`(data, src, labels[, subject, subjects_dir])	Fit the aggregation step to the provided data, source space, and labels.
`fit_transform`(data, src, labels[, subject, ...])	Fit the aggregation step to the provided data, source space, and labels, and apply the aggregation to extract the ROI time courses.
`get_names`()	Label names are used as names for rows of the weight matrix.
`get_params`()	Get the single `surf` parameter of the aggregation step as a dictionary.
`get_weights`()	The weight matrix corresponding to the resulting aggregation transformation.
`transform`(data)	Apply centroid-based aggregation to the provided data.

fit(data, src, labels, subject=None, subjects_dir=None)#

Fit the aggregation step to the provided data, source space, and labels.

Parameters:

dataSourceEstimate: The source estimate containing the reconstructed source time courses.
srcSourceSpaces: The definition of the considered source space for inverse modeling.
labelsLabel | list of Label: The label or list of labels defining the ROIs for which time courses should be extracted.
subjectstr | None: The subject name. If None, it will be inferred from the source space.
subjects_dirstr | None: The directory containing the subjects’ MRI data. If None, it will be inferred from the environment by MNE-Python. Set the path explicitly in case of errors.

Returns:

selfCentroidAggregation: The fitted aggregation step.

fit_transform(data, src, labels, subject=None, subjects_dir=None)#: Fit the aggregation step to the provided data, source space, and labels, and apply the aggregation to extract the ROI time courses. See fit() and transform() for details on the parameters and return values, respectively.

get_names()#

Label names are used as names for rows of the weight matrix.

Returns:

nameslist of str: The names of the rows / labels.

get_params()#

Get the single surf parameter of the aggregation step as a dictionary.

Returns:

paramsdict: The parameters of the aggregation step.

get_weights()#

The weight matrix corresponding to the resulting aggregation transformation.

Returns:

weightsarray: The weight matrix that contains one non-zero entry per row corresponding to the selected centroid source for each label.

transform(data)#

Apply centroid-based aggregation to the provided data.

Parameters:

dataSourceEstimate: The source estimate containing the reconstructed source time courses.

Returns:

label_tcarray, shape (n_labels, n_times): The extracted time courses for each label.